bliss-rs/README.md

153 lines
5.9 KiB
Markdown
Raw Normal View History

[![crate](https://img.shields.io/crates/v/bliss-audio.svg)](https://crates.io/crates/bliss-audio)
[![build](https://github.com/Polochon-street/bliss-rs/workflows/Rust/badge.svg)](https://github.com/Polochon-street/bliss-rs/actions)
[![doc](https://docs.rs/bliss-audio/badge.svg)](https://docs.rs/bliss-audio/)
2021-05-14 17:35:08 +03:00
# bliss music analyzer - Rust version
bliss-rs is the Rust improvement of [bliss](https://github.com/Polochon-street/bliss), a
library used to make playlists by analyzing songs, and computing distance between them.
2021-05-14 17:35:08 +03:00
Like bliss, it eases the creation of « intelligent » playlists and/or continuous
play, à la Spotify/Grooveshark Radio, as well as easing creating plug-ins for
existing audio players. For instance, you can use it to make calm playlists
to help you sleeping, fast playlists to get you started during the day, etc.
2021-05-14 17:35:08 +03:00
For now (and if you're looking for an easy-to use smooth play experience),
[blissify](https://crates.io/crates/blissify) implements bliss for
[MPD](https://www.musicpd.org/).
There are also [python](https://pypi.org/project/bliss-audio/) bindings.
Note 1: the features bliss-rs outputs is not compatible with the ones
used by C-bliss, since it uses
different, more accurate values, based on
[actual literature](https://lelele.io/thesis.pdf). It is also faster.
## Examples
2021-11-27 14:13:54 +02:00
For simple analysis / distance computing, take a look at `examples/distance.rs` and
`examples/analyze.rs`.
2021-05-14 17:35:08 +03:00
2021-11-27 14:13:54 +02:00
If you simply want to try out making playlists from a folder containing songs,
[this example](https://github.com/Polochon-street/bliss-rs/blob/master/examples/playlist.rs)
contains all you need. Usage:
cargo run --features=serde --release --example=playlist /path/to/folder /path/to/first/song
Don't forget the `--release` flag!
By default, it outputs the playlist to stdout, but you can use `-o <path>`
to output it to a specific path.
To avoid having to analyze the entire folder
several times, it also stores the analysis in `/tmp/analysis.json`. You can customize
this behavior by using `-a <path>` to store this file in a specific place.
Ready to use code examples:
### Compute the distance between two songs
```
2021-06-08 22:40:46 +03:00
use bliss_audio::{BlissError, Song};
2021-06-08 22:40:46 +03:00
fn main() -> Result<(), BlissError> {
let song1 = Song::new("/path/to/song1")?;
let song2 = Song::new("/path/to/song2")?;
println!("Distance between song1 and song2 is {}", song1.distance(&song2));
Ok(())
}
```
### Make a playlist from a song
```
2021-06-08 22:40:46 +03:00
use bliss_audio::{BlissError, Song};
use noisy_float::prelude::n32;
fn main() -> Result<(), BlissError> {
let paths = vec!["/path/to/song1", "/path/to/song2", "/path/to/song3"];
let mut songs: Vec<Song> = paths
.iter()
.map(|path| Song::new(path))
.collect::<Result<Vec<Song>, BlissError>>()?;
// Assuming there is a first song
2021-06-08 22:40:46 +03:00
let first_song = songs.first().unwrap().to_owned();
songs.sort_by_cached_key(|song| n32(first_song.distance(&song)));
println!(
"Playlist is: {:?}",
songs
.iter()
.map(|song| &song.path)
.collect::<Vec<&String>>()
);
Ok(())
}
```
## Further use
Instead of reinventing ways to fetch a user library, play songs, etc,
and embed that into bliss, it is easier to look at the
2021-11-27 14:13:54 +02:00
[Library](https://docs.rs/bliss-audio/0.4.1/bliss_audio/library/trait.Library.html)
trait.
By implementing a few functions to get songs from a media library, and store
the resulting analysis, you get access to functions to analyze an entire
library (with multithreading), and to make playlists easily.
See [blissify](https://crates.io/crates/blissify) for a reference
implementation.
## Cross-compilation
To cross-compile bliss-rs from linux to x86_64 windows, install the
`x86_64-pc-windows-gnu` target via:
rustup target add x86_64-pc-windows-gnu
Make sure you have `x86_64-w64-mingw32-gcc` installed on your computer.
Then after downloading and extracting [ffmpeg's prebuilt binaries](https://www.gyan.dev/ffmpeg/builds/),
running:
FFMPEG_DIR=/path/to/prebuilt/ffmpeg cargo build --target x86_64-pc-windows-gnu --release
Will produce a `.rlib` library file. If you want to generate a shared `.dll`
library, add:
[lib]
crate-type = ["cdylib"]
to `Cargo.toml` before compiling, and if you want to generate a `.lib` static
library, add:
[lib]
crate-type = ["staticlib"]
You can of course test the examples yourself by compiling them as .exe:
FFMPEG_DIR=/path/to/prebuilt/ffmpeg cargo build --target x86_64-pc-windows-gnu --release --examples
WARNING: Doing all of the above and making it work on windows requires to have
ffmpeg's dll on your Windows `%PATH%` (`avcodec-59.dll`, etc).
Usually installing ffmpeg on the target windows is enough, but you can also just
extract them from `/path/to/prebuilt/ffmpeg/bin` and put them next to the thing
you generated from cargo (either bliss' dll or executable).
2021-05-14 17:35:08 +03:00
## Acknowledgements
* This library relies heavily on [aubio](https://aubio.org/)'s
[Rust bindings](https://crates.io/crates/aubio-rs) for the spectral /
timbral analysis, so a big thanks to both the creators and contributors
of librosa, and to @katyo for making aubio bindings for Rust.
* The first part of the chroma extraction is basically a rewrite of
[librosa](https://librosa.org/doc/latest/index.html)'s
[chroma feature extraction](https://librosa.org/doc/latest/generated/librosa.feature.chroma_stft.html?highlight=chroma#librosa.feature.chroma_stftfrom)
from python to Rust, with just as little features as needed. Thanks
to both creators and contributors as well.
* Finally, a big thanks to
[Christof Weiss](https://www.audiolabs-erlangen.de/fau/assistant/weiss)
for pointing me in the right direction for the chroma feature summarization,
which are basically also a rewrite from Python to Rust of some of the
awesome notebooks by AudioLabs Erlangen, that you can find
[here](https://www.audiolabs-erlangen.de/resources/MIR/FMP/C0/C0.html).